1.0.0 • Public • Published


Inline TensorFlowJS models in your app bundle. Simply import weight manifest .bin file and get associated model.json as a blob URL

Ideal for delivering your machine-learning inference application as a single .js for script tag injection


Install the plugin :

npm i parcel-plugin-tensorflow-blob

Import a single model and load it in TensorFlowJS

You might want to have this kind of path in your project:

|-- model
|   |-- group1-shard1of1.bin
|   `-- model.json
import modelWeightManifest from "model/group1-shard1of1.bin"
const modelTopologyBlobURL = modelWeightManifest.blobModelPath()

mySuperModel = await tf.loadLayersModel(modelTopologyBlobURL)

Import multiple models at once

|-- models
|   |-- model1
|   |   |-- group1-shard1of1.bin
|   |   `-- model.json
|   |-- model2
|   |   |-- group1-shard1of1.bin
|   |   `-- model.json
|   `-- model3
|       |-- group1-shard1of1.bin
|       `-- model.json

use globs :

import modelWeightManifests from "models/**/*.bin"

How does it works ?

This script embbeds bin weight files as Bas64 encoded string and model.json as string at bundling time. It wraps them in a function (blobModelPath). Calling this function on imported .bin asset:

  • Decodes the base64 string
  • Constructs a blob object for the decoded bin and a blob URL for it
  • Edits model.json with the weight manifest blob
  • Constructs a blob object for the model.json
  • Returns the model.json's blob URL, which is obviously related to

Fits your needs ?

Glad ! I hope this plugin helps you focus on building models and evaporates all the burden of building distribuable TensorFlowJS apps or libraries

If not, look for IOHandler documentation in TensorFlowJS. SPOILER : It's quirky and motivated me to write this parcel plugin ;)

You might also contribute to this project.

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npm i parcel-plugin-tensorflowjs-blob

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  • damienlaine